{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Word2Vec.jl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using Word2Vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Training\n",
    "\n",
    "Three functions are available for traning:\n",
    "\n",
    " * ``word2vec``\n",
    " * ``word2phrase``\n",
    " * ``word2cluster``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first download a text corpus:  http://mattmahoney.net/dc/text8.zip and unzip it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All functions are documented, i.e., we can type ``?functionname`` to check input options. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: "
     ]
    },
    {
     "data": {
      "text/latex": [
       "\\texttt{word2vec(train, output; size=100, window=5, sample=1e-3, hs=0,  negative=5, threads=12, iter=5, min_count=5, alpha=0.025, debug=2, binary=1, cbow=1, save_vocal=Void(), read_vocab=Void(), verbose=false,)}\n",
       "\\begin{verbatim}\n",
       "Parameters for training:\n",
       "    train <file>\n",
       "        Use text data from <file> to train the model\n",
       "    output <file>\n",
       "        Use <file> to save the resulting word vectors / word clusters\n",
       "    size <Int>\n",
       "        Set size of word vectors; default is 100\n",
       "    window <Int>\n",
       "        Set max skip length between words; default is 5\n",
       "    sample <AbstractFloat>\n",
       "        Set threshold for occurrence of words. Those that appear with\n",
       "        higher frequency in the training data will be randomly\n",
       "        down-sampled; default is 1e-5.\n",
       "    hs <Int>\n",
       "        Use Hierarchical Softmax; default is 1 (0 = not used)\n",
       "    negative <Int>\n",
       "        Number of negative examples; default is 0, common values are \n",
       "        5 - 10 (0 = not used)\n",
       "    threads <Int>\n",
       "        Use <Int> threads (default 12)\n",
       "    iter <Int>\n",
       "        Run more training iterations (default 5)\n",
       "    min_count <Int>\n",
       "        This will discard words that appear less than <Int> times; default\n",
       "        is 5\n",
       "    alpha <AbstractFloat>\n",
       "        Set the starting learning rate; default is 0.025\n",
       "    debug <Int>\n",
       "        Set the debug mode (default = 2 = more info during training)\n",
       "    binary <Int>\n",
       "        Save the resulting vectors in binary moded; default is 0 (off)\n",
       "    cbow <Int>\n",
       "        Use the continuous back of words model; default is 1 (skip-gram\n",
       "        model)\n",
       "    save_vocab <file>\n",
       "        The vocabulary will be saved to <file>\n",
       "    read_vocab <file>\n",
       "        The vocabulary will be read from <file>, not constructed from the\n",
       "        training data\n",
       "    verbose <Bool>\n",
       "        Print output from training\n",
       "\\end{verbatim}\n"
      ],
      "text/markdown": [
       "`word2vec(train, output; size=100, window=5, sample=1e-3, hs=0,  negative=5, threads=12, iter=5, min_count=5, alpha=0.025, debug=2, binary=1, cbow=1, save_vocal=Void(), read_vocab=Void(), verbose=false,)`\n",
       "\n",
       "```\n",
       "Parameters for training:\n",
       "    train <file>\n",
       "        Use text data from <file> to train the model\n",
       "    output <file>\n",
       "        Use <file> to save the resulting word vectors / word clusters\n",
       "    size <Int>\n",
       "        Set size of word vectors; default is 100\n",
       "    window <Int>\n",
       "        Set max skip length between words; default is 5\n",
       "    sample <AbstractFloat>\n",
       "        Set threshold for occurrence of words. Those that appear with\n",
       "        higher frequency in the training data will be randomly\n",
       "        down-sampled; default is 1e-5.\n",
       "    hs <Int>\n",
       "        Use Hierarchical Softmax; default is 1 (0 = not used)\n",
       "    negative <Int>\n",
       "        Number of negative examples; default is 0, common values are \n",
       "        5 - 10 (0 = not used)\n",
       "    threads <Int>\n",
       "        Use <Int> threads (default 12)\n",
       "    iter <Int>\n",
       "        Run more training iterations (default 5)\n",
       "    min_count <Int>\n",
       "        This will discard words that appear less than <Int> times; default\n",
       "        is 5\n",
       "    alpha <AbstractFloat>\n",
       "        Set the starting learning rate; default is 0.025\n",
       "    debug <Int>\n",
       "        Set the debug mode (default = 2 = more info during training)\n",
       "    binary <Int>\n",
       "        Save the resulting vectors in binary moded; default is 0 (off)\n",
       "    cbow <Int>\n",
       "        Use the continuous back of words model; default is 1 (skip-gram\n",
       "        model)\n",
       "    save_vocab <file>\n",
       "        The vocabulary will be saved to <file>\n",
       "    read_vocab <file>\n",
       "        The vocabulary will be read from <file>, not constructed from the\n",
       "        training data\n",
       "    verbose <Bool>\n",
       "        Print output from training\n",
       "```\n"
      ],
      "text/plain": [
       "`word2vec(train, output; size=100, window=5, sample=1e-3, hs=0,  negative=5, threads=12, iter=5, min_count=5, alpha=0.025, debug=2, binary=1, cbow=1, save_vocal=Void(), read_vocab=Void(), verbose=false,)`\n",
       "\n",
       "```\n",
       "Parameters for training:\n",
       "    train <file>\n",
       "        Use text data from <file> to train the model\n",
       "    output <file>\n",
       "        Use <file> to save the resulting word vectors / word clusters\n",
       "    size <Int>\n",
       "        Set size of word vectors; default is 100\n",
       "    window <Int>\n",
       "        Set max skip length between words; default is 5\n",
       "    sample <AbstractFloat>\n",
       "        Set threshold for occurrence of words. Those that appear with\n",
       "        higher frequency in the training data will be randomly\n",
       "        down-sampled; default is 1e-5.\n",
       "    hs <Int>\n",
       "        Use Hierarchical Softmax; default is 1 (0 = not used)\n",
       "    negative <Int>\n",
       "        Number of negative examples; default is 0, common values are \n",
       "        5 - 10 (0 = not used)\n",
       "    threads <Int>\n",
       "        Use <Int> threads (default 12)\n",
       "    iter <Int>\n",
       "        Run more training iterations (default 5)\n",
       "    min_count <Int>\n",
       "        This will discard words that appear less than <Int> times; default\n",
       "        is 5\n",
       "    alpha <AbstractFloat>\n",
       "        Set the starting learning rate; default is 0.025\n",
       "    debug <Int>\n",
       "        Set the debug mode (default = 2 = more info during training)\n",
       "    binary <Int>\n",
       "        Save the resulting vectors in binary moded; default is 0 (off)\n",
       "    cbow <Int>\n",
       "        Use the continuous back of words model; default is 1 (skip-gram\n",
       "        model)\n",
       "    save_vocab <file>\n",
       "        The vocabulary will be saved to <file>\n",
       "    read_vocab <file>\n",
       "        The vocabulary will be read from <file>, not constructed from the\n",
       "        training data\n",
       "    verbose <Bool>\n",
       "        Print output from training\n",
       "```\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word2vec Word2Vec\n",
      "\n"
     ]
    }
   ],
   "source": [
    "?word2vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training using file Downloads/text8\n",
      "Vocab size: 71291\n",
      "Words in train file: 16718843\n",
      "Alpha: 0.000002  Progress: 100.03%  Words/thread/sec: 356.11k  "
     ]
    }
   ],
   "source": [
    "word2vec(\"Downloads/text8\", \"text8-vec.txt\", verbose=true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will create a text file ``text8-vec.txt`` where each word in ``text8``\n",
    "is represented by a vector. In certain applications, we want to have vector\n",
    "representation of larger piece of text. For example, instead of considering ``\"san\"`` and ``\"francisco\"`` as two words, we want to have a vector to represent ``\"san francisco\"``. This can be achieved by pre-processing the text corpus with the function ``word2phrase``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training using file Downloads/text8\n",
      "\n",
      "Vocab size (unigrams + bigrams): 2419827\n",
      "Words in train file: 17005206\n",
      "Starting training using file text8phrase\n",
      "Vocab size: 98331\n",
      "Words in train file: 15857306\n",
      "Alpha: 0.000002  Progress: 100.03%  Words/thread/sec: 341.90k  "
     ]
    }
   ],
   "source": [
    "word2phrase(\"Downloads/text8\", \"text8phrase\")\n",
    "word2vec(\"text8phrase\", \"text8phrase-vec.txt\", verbose=true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`word2clusters` gives each word a class ID number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training using file text8\n",
      "Vocab size: 71291\n",
      "Words in train file: 16718843\n",
      "Alpha: 0.000006  Progress: 99.99%  Words/thread/sec: 382.50k  "
     ]
    }
   ],
   "source": [
    "word2clusters(\"text8\", \"text8-class.txt\", 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text8\n",
      "text8-class.txt\n",
      "text8phrase\n",
      "text8phrase-vec.txt\n",
      "text8-vec.txt\n",
      "text8.zip\n"
     ]
    }
   ],
   "source": [
    ";ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WordVectors 71291 words, 100-element Float64 vectors"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = wordvectors(\"text8-vec.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are some basic functionalities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,71291)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71291-element Array{AbstractString,1}:\n",
       " \"</s>\"        \n",
       " \"the\"         \n",
       " \"of\"          \n",
       " \"and\"         \n",
       " \"one\"         \n",
       " \"in\"          \n",
       " \"a\"           \n",
       " \"to\"          \n",
       " \"zero\"        \n",
       " \"nine\"        \n",
       " \"two\"         \n",
       " \"is\"          \n",
       " \"as\"          \n",
       " ⋮             \n",
       " \"raam\"        \n",
       " \"barad\"       \n",
       " \"baume\"       \n",
       " \"mothmen\"     \n",
       " \"gallopin\"    \n",
       " \"horsecollar\" \n",
       " \"mojitos\"     \n",
       " \"snaggletooth\"\n",
       " \"introvigne\"  \n",
       " \"denishawn\"   \n",
       " \"tamiris\"     \n",
       " \"dolophine\"   "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = vocabulary(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = index(model, \"book\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"book\""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can retrieve the vector representation of individual words and compute the cosine distance between two words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100-element Array{Float64,1}:\n",
       " -0.00124171 \n",
       " -0.153338   \n",
       "  0.102503   \n",
       "  0.0189016  \n",
       "  0.0481557  \n",
       " -0.017203   \n",
       " -0.0345992  \n",
       " -0.143795   \n",
       "  0.13964    \n",
       "  0.10404    \n",
       "  0.0987664  \n",
       "  0.000247274\n",
       " -0.0294016  \n",
       "  ⋮          \n",
       " -0.0729129  \n",
       "  0.00609002 \n",
       " -0.115113   \n",
       " -0.1635     \n",
       "  0.104623   \n",
       " -0.0815325  \n",
       " -0.0979441  \n",
       " -0.0522775  \n",
       " -0.0893822  \n",
       "  0.121403   \n",
       " -0.0100501  \n",
       "  0.100918   "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_vector(model, \"one\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1-element Array{Float64,1}:\n",
       " 0.795706"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity(model, \"one\", \"two\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1-element Array{Float64,1}:\n",
       " 0.000406437"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity(model, \"one\", \"hello\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The funciton ``cosine(model, word, n)`` return the indices and distances\n",
    "of `n` neighbors of ``word``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1056,5356,3222,6964,4611,9122,4219,4218,12359,15749],[0.9999999999999999,0.7663500088541577,0.754464599846764,0.7269210839899815,0.6949826015235541,0.6906811525014919,0.6899940820154246,0.6874193547947338,0.6820260362215733,0.6817421670970778])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs, dists = cosine(model, \"paris\", 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use ``Gadfly`` to plot the top 10 similar words to ``\"paris\"`` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using Gadfly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": [
       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       "f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\n",
       "this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on(\"snap.drag.start.\"+this.id,d);b&&k.on(\"snap.drag.move.\"+this.id,b);f&&k.on(\"snap.drag.end.\"+this.id,f);k(\"snap.drag.start.\"+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?\"T\":\"t\")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};\n",
       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
       "function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\n",
       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-290c273879864d4180d8af9421565081\");\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-4\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-13\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-13\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-13\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-15\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-15\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-15\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-16\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-16\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-16\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-16\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-16\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-17\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-17\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#fig-290c273879864d4180d8af9421565081-element-17\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(x=words[idxs], y=dists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: "
     ]
    },
    {
     "data": {
      "text/latex": [
       "\\texttt{analogy(wv, pos, neg, n=5)}\n",
       "Compute the analogy similarity between two lists of words. The positions and the similarity values of the top \\texttt{n} similar words will be returned.  For example,  \\texttt{king - man + woman = queen} will be  \\texttt{pos=[\"king\", \"woman\"], neg=[\"man\"]}.\n"
      ],
      "text/markdown": [
       "`analogy(wv, pos, neg, n=5)`\n",
       "\n",
       "Compute the analogy similarity between two lists of words. The positions and the similarity values of the top `n` similar words will be returned.  For example,  `king - man + woman = queen` will be  `pos=[\"king\", \"woman\"], neg=[\"man\"]`.\n"
      ],
      "text/plain": [
       "`analogy(wv, pos, neg, n=5)`\n",
       "\n",
       "Compute the analogy similarity between two lists of words. The positions and the similarity values of the top `n` similar words will be returned.  For example,  `king - man + woman = queen` will be  `pos=[\"king\", \"woman\"], neg=[\"man\"]`.\n"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analogy analogy_words\n",
      "\n"
     ]
    }
   ],
   "source": [
    "?analogy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([904,6854,1062,2033,527,12269,2076,3422],[0.29024117726721255,0.26277586168028433,0.253278904324895,0.25208853175214935,0.24775773633691375,0.24558402441677105,0.24309061947916874,0.2418303817434562])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indxs, dists = analogy(model, [\"king\", \"woman\"], [\"man\"], 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": [
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       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
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       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44\");\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-4\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-7\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-7\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-8\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-8\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-13\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-13\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-13\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-15\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-15\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-15\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-16\")\n",
       "   .data(\"max_pos\", 120.42)\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-16\")\n",
       "   .data(\"min_pos\", 103.42)\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-16\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-16\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-16\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-17\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-17\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#fig-e15f79f53c664b7ba7a1c6a9bd3acb44-element-17\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(x=words[indxs], y=dists)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``analogy_words`` is a wrapper of ``analogy``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: "
     ]
    },
    {
     "data": {
      "text/latex": [
       "\\texttt{analogy_words(wv, pos, neg, n=5)}\n",
       "Return the top \\texttt{n} words computed by analogy similarity between positive words \\texttt{pos} and negaive words \\texttt{neg}. from the WordVectors \\texttt{wv}. \n"
      ],
      "text/markdown": [
       "`analogy_words(wv, pos, neg, n=5)`\n",
       "\n",
       "Return the top `n` words computed by analogy similarity between positive words `pos` and negaive words `neg`. from the WordVectors `wv`. \n"
      ],
      "text/plain": [
       "`analogy_words(wv, pos, neg, n=5)`\n",
       "\n",
       "Return the top `n` words computed by analogy similarity between positive words `pos` and negaive words `neg`. from the WordVectors `wv`. \n"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analogy_words\n",
      "\n"
     ]
    }
   ],
   "source": [
    "?analogy_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10-element Array{AbstractString,1}:\n",
       " \"berlin\"    \n",
       " \"munich\"    \n",
       " \"leipzig\"   \n",
       " \"vienna\"    \n",
       " \"bonn\"      \n",
       " \"dresden\"   \n",
       " \"hamburg\"   \n",
       " \"stuttgart\" \n",
       " \"frankfurt\" \n",
       " \"heidelberg\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy_words(model, [\"paris\", \"germany\"], [\"france\"], 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WordVectors 98331 words, 100-element Float64 vectors"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = wordvectors(\"text8phrase-vec.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``model2`` is pre-processed by ``word2phrase``, so we can compute the similar words of phrases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13-element Array{AbstractString,1}:\n",
       " \"los_angeles\"  \n",
       " \"san_francisco\"\n",
       " \"san_diego\"    \n",
       " \"miami\"        \n",
       " \"las_vegas\"    \n",
       " \"seattle\"      \n",
       " \"cincinnati\"   \n",
       " \"cleveland\"    \n",
       " \"st_louis\"     \n",
       " \"california\"   \n",
       " \"chicago\"      \n",
       " \"dallas\"       \n",
       " \"atlanta\"      "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similar_words(model2, \"los_angeles\", 13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WordClusters 71291 words, 100 clusters"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = wordclusters(\"text8-class.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function ``clusters`` returns all the clusters in a model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100-element Array{Integer,1}:\n",
       "  0\n",
       "  1\n",
       "  2\n",
       "  3\n",
       "  4\n",
       "  5\n",
       "  6\n",
       "  7\n",
       "  8\n",
       "  9\n",
       " 10\n",
       " 11\n",
       " 12\n",
       "  ⋮\n",
       " 88\n",
       " 89\n",
       " 90\n",
       " 91\n",
       " 92\n",
       " 93\n",
       " 94\n",
       " 95\n",
       " 96\n",
       " 97\n",
       " 98\n",
       " 99"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters(model3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use ``get_cluster`` to retrieve the cluster ID of a given word and use ``get_words`` to retrieve all the words\n",
    "of a given cluster ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_cluster(model3, \"two\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116-element Array{AbstractString,1}:\n",
       " \"one\"          \n",
       " \"zero\"         \n",
       " \"nine\"         \n",
       " \"two\"          \n",
       " \"eight\"        \n",
       " \"five\"         \n",
       " \"three\"        \n",
       " \"four\"         \n",
       " \"six\"          \n",
       " \"seven\"        \n",
       " \"years\"        \n",
       " \"th\"           \n",
       " \"century\"      \n",
       " ⋮              \n",
       " \"interceptions\"\n",
       " \"nisan\"        \n",
       " \"weekday\"      \n",
       " \"ramadan\"      \n",
       " \"weekdays\"     \n",
       " \"workday\"      \n",
       " \"thirtieth\"    \n",
       " \"lunations\"    \n",
       " \"graders\"      \n",
       " \"goodwrench\"   \n",
       " \"spoked\"       \n",
       " \"rublei\"       "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_words(model3, 39)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.4.0",
   "language": "julia",
   "name": "julia-0.4"
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   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.4.0"
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